the design and implementation of an energy-smart home in korea
TRANSCRIPT
Copyright 2013. The Korean Institute of Information Scientists and Engineers pISSN: 1976-4677 eISSN: 2093-8020
Journal of Computing Science and Engineering,Vol. 7, No. 3, September 2013, pp. 204-210
Invited Paper
The Design and Implementation of an Energy-Smart Home inKorea
Jin Xiao*
Division of IT Convergence Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea
Raouf Boutaba
Division of IT Convergence Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea
School of Computer Science, University of Waterloo, Waterloo, ON, Canada
AbstractWe present the motivation, design and implementation of a smart home system in Korea. Our system is open, extensible,
integrated, intelligent, and usage-centric. We detail the challenges and key design requirements for the smart home sys-
tem based on our past experiences, and show how convergence system design is a capable methodology for enabling an
integrated and multi-faceted home management system that encompasses energy management, home appliance control,
environment management, u-health, and living support functionalities under a single unified design. Using energy man-
agement as a specific case study, we demonstrate how convergence system design can encapsulate technology heteroge-
neity and hardware-software disparity without compromising simple yet powerful user interfaces.
Category: Convergence computing
Keywords: Smart home; Energy management; u-Health
I. INTRODUCTION
A. Overview of POSTECH Smart Home
With the accelerated introduction of powerful electron-
ics, computing devices, and communication infrastruc-
tures to homes today, the concept of homes is involving.
Home electronics are acquiring advanced communication
and computing capabilities, and various new environmen-
tal, medical, and energy sensors are making their way
into home deployment. Virtually all modern home devices
can communicate and exchange information.
At the Division of IT Convergence Engineering of
Pohang University of Science and Technology (POSTECH),
we have designed and constructed a modern home
equipped with diverse sensors, modern appliances, and
home area networking infrastructures (power line com-
munication, Ethernet, Wi-Fi, and ZigBee). Fig. 1 shows
an actual view of the smart home. We have designed and
are implementing a POSTECH Smart Home system that
challenges this research frontier, and seek to obtain
answers as to what functions are needed and how to realize
them. The three areas we are examining in the POSTECH
Smart Home are energy management, u-health, and envi-
Received 12 June 2013, Accepted 4 July 2013
*Corresponding Author
Open Access http://dx.doi.org/10.5626/JCSE.2013.7.3.204 http://jcse.kiise.orgThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
The Design and Implementation of an Energy-Smart Home in Korea
Jin Xiao and Raouf Boutaba 205 http://jcse.kiise.org
ronment control and protection.
Previous work on smart home system design has gen-
erally focused on a specific problem area such as infor-
mation correlation [1] or hardware (e.g., [2]), and therefore
did not take on a holistic system engineering view. In this
paper, we present the POSTECH Smart Home architec-
ture, and discuss key design characteristics, user-friendly
user-interfacing (UI) design, and non-intrusive network
architecture. We also detail the design and implementa-
tion of an energy management solution currently being
deployed to better illustrate the challenges that must be
addressed in smart home research.
We found that there are four major key design require-
ments that every smart home of the future should meet:
1) User-friendliness: This is an often overlooked design
criterion in many areas of system engineering. The
user-friendliness we are interested in extends beyond
UI issues, to how we can develop functionality that
is most comfortable and helpful to typical, often
non-technical, home occupants. In this regard, we
find that timely and focused functionality combined
with an easy-to-use interface achieves the best
reception among households. However, it is not easy
to obtain timely and focused system design, as it relies
on a rather high degree of intelligent automation.
2) Intelligence: Intelligent automation is what general
households expect from future smart homes. How-
ever, how to be intelligent in the correct way is a key
issue for systems and application developers. In the
context of the home, we find that intelligence gains
the most appreciation when automation is provided
for the most basic and sensible functions (such as
turning on lights when coming home, and turning
them off when going to sleep). The key enabler of
such intelligent automation is not only algorithmic,
but also extremely knowledge-centric. The mere
generation of user context or intent prediction (e.g.,
“I want to go to sleep”) requires complex informa-
tion processing of diverse information sources.
3) Non-intrusiveness: Another aspect of user friendli-
ness and intelligence comes from the ability of the
technology to operate in the background. House
occupants should not be constantly reminded of the
need to command or interact with machines, and
should not be bothered by streams of notifications
and queries for the most basic and routine functions.
Sometimes, it is better not to automate, and even to
make imprecise decisions (for very simple, basic
tasks), than to bother users for precise instructions.
4) Security: Security and its accompanying factor, pri-
vacy, are extremely important for the adoption of
any smart home system.
II. SMART HOME SYSTEM ARCHITECTURE
In this section, we present the smart home system
architecture (Fig. 2) that satisfies the key design factors
outlined in Section I. The entire system is roughly com-
posed of five substrates: device adaptation, knowledge
base, decision and policy engine, service managers, and
UI device integration. The entire system implementation
is supported by Java Spring Framework [3], a lightweight
open-source application framework for enterprise devel-
opment, based on code published in ‘Expert One-on-One
J2EE Design and Development’ by Rod Johnson. The
Spring Framework is capable of integrating with various
third-party frameworks and libraries. It comprises a data
access framework, remote access framework, aspect-ori-
ented programming framework, model-view-controller
framework, and batch framework.
Fig. 1. Pohang University of Science and Technology (POSTECH) Smart Home deployment.
Journal of Computing Science and Engineering, Vol. 7, No. 3, September 2013, pp. 204-210
http://dx.doi.org/10.5626/JCSE.2013.7.3.204 206 Jin Xiao and Raouf Boutaba
A. Device Adaptors
The device adaptors are the essential gateway between
the smart home system and the hardware sensor devices.
As such, they are responsible for providing technology-
neutral interfacing between the devices (sensors and actu-
ators) and the software system. This is an essential aspect
of the adaptor design, because of the vast heterogeneity
that exists among the different sensor devices, which
include different communication protocols, message for-
mats, data representations, control and query semantics,
etc. The adaptors are device-class specific, meaning that
for a specific product, a specific software adaptor needs
to be developed. For our smart home, we have developed
an adaptor for two different types of ZigBee protocols,
for Samsung appliances, and for Bluetooth-enabled envi-
ronmental sensing and control devices. Fig. 2 showcases
some of the communication technologies we needed to
adapt to, including Bluetooth, ZigBee, and Wi-Fi.
B. Knowledge Base
The successful adaptation and intelligent automation
of a smart home relies on the ability of the smart home
system to organize, process, and analyze different sources
of information to drive the automation decision-making
and user context determination. To this end, a strong and
formal support for the knowledge base is central to the
system design. The knowledge base is a vital design chal-
lenge for our smart home system. We have constructed
formal information models that specify the relationship,
format, semantics, and context of each information source
(e.g., environmental information, energy information, appli-
ance usage data, appliance control information). We have
also created a relational database structure that supports
publish/subscribe semantics, meaning that software ser-
vices or devices can subscribe to specific knowledge
events in the database (e.g., a particular metric value has
been changed/updated, a particular user context has been
fulfilled). Three layers of information are organized in the
database:
1) Sensor information: This is the basic sensor infor-
mation gathered from specific sensor devices. This
sensor information is not distilled and unrelated to
each other. For processing efficiency, we also cre-
ated aggregate classes of the base metrics such that
aggregate values are generated (e.g., average, max.,
min., etc.) across a larger time interval than the sen-
sor’s sampling intervals.
2) Context information: The context information is
generated by correlating multiple sensor information
metrics under a specific criterion. Context informa-
tion is model-driven by nature, in that to obtain spe-
cific context information (e.g., that the user is
sleeping), we derive specific computational models
that relate multiple pieces of sensor information
together in a mathematically structured process.
3) Rule-driven intelligence: The intelligence of the
system comes from the generation of rules or poli-
cies that specify the condition-action pairs among
sensor information, context information, and control
actions.
Fig. 2. Smart home system (SHS) architecture.
The Design and Implementation of an Energy-Smart Home in Korea
Jin Xiao and Raouf Boutaba 207 http://jcse.kiise.org
C. Decision and Policy Engine
The decision and policy engine in our smart home sys-
tem is responsible for the intelligent automation and
home-directed management. This functionality is sup-
ported by an event-condition-action (ECA) rule engine:
● Event: specifies the signal that triggers the invoca-
tion of the rule.● Condition: a logical test that, if satisfied or evaluated
to be true, causes the action to be carried out.● Action: consists of database object manipulation,
management actions, and device information and
command exchanges.
Fig. 3 shows the rule engine’s process flow. Once an
event occurs, a condition evaluator will perform a logical
test on the event using defined policy rules. The policy
rules are defined by homes or are built into the system
using an extensible markup language (XML)-based scheme
and interpreted by the ECA rule parser. The matching
ECA rule is then invoked by the condition evaluator. If
the event satisfies the condition set, the corresponding
actions are scheduled by the action scheduler. As a part of
the action, information from the knowledge base may be
accessed and updated.
The ECA rule engine is implemented using a Java-
based rule engine called Drools [4]. Drools uses a for-
ward chaining inference engine that relies on the Rete
algorithm for pattern matching. There are five sub-mod-
ules in Drools, and our system uses Drools Expert mod-
ule. Fig. 3 shows an example syntax of Drool rules.
D. Service Managers
The service managers are where the domain-specific
management intelligence, analytics, and control logics
reside. In the next section, we will show some design spe-
cifics for home energy management and much of the pro-
cesses, data views, and intelligent scheduling functions
implemented as an energy manager. The service manag-
ers interact with multiple component groups in the smart
home system, including exchange with adaptors through
adaptor interfaces to retrieve, configure, and execute
device commands; interaction with server stubs to drive
the UI and push selective information; querying the
knowledge base for specific metrics and rule sets, and
updating them based on intelligent processing and algo-
rithms; modifying rules through the decision engine.
E. Multi-Modal UI
The abundance of multiple digital displays and interac-
tive media in today’s homes calls for multi-modal user
interface design, to facilitate the comfortable and easy
use of a smart home system. Our smart home service uti-
lizes smartphones for information presentation and home
management, as well as televisions for augmented dis-
play and presentation. Fig. 4 shows the technology imple-
mentation that realizes this multi-modal UI design. We
utilize an Adobe technology platform consisting of a
Flash Player and Adobe AIR. A flex framework is built
on top of the Flash Player application programming inter-
faces, and runs both Flash and Adobe AIR. It allows for a
unified UI design that can be supported across multiple
display platforms such as TVs, desktop computers, and
smartphones. In the backend, a BlazeDS server [5],
which is a server-based Java remoting and Web messag-
ing technology, allows developers to connect to back-end
service logic and databases.
Fig. 5 shows some of the UI screens of our system. The
top two sub-figures show the login screen for the TV and
the smartphone, while the bottom two show the home
screens of the TV and the smartphone after successful
login. The multi-modal UI design allows for information
augmentation and synchronization between the TV dis-
play and the smartphone, while the actual control is com-
Fig. 3. Event-condition-action (ECA) rule engine.
Journal of Computing Science and Engineering, Vol. 7, No. 3, September 2013, pp. 204-210
http://dx.doi.org/10.5626/JCSE.2013.7.3.204 208 Jin Xiao and Raouf Boutaba
pletely implemented through the smartphone, allowing
the home user to control, view, and interact with their
home anywhere within the home, or even outside. The
home system currently also supports voice synthesis
reporting of key messages and events, and in the future,
will support voice commands. Security to system access
is reinforced through a TripleSafe design that employs
user password login (Fig. 5), radio-frequency identifica-
tion-based person tagging (at the home entrance), and
smartphone-enabled facial recognition. We utilize all three
safeguards to secure access to information, control, and
device operations.
Fig. 4. Technologies supporting multi-modal user interface. MXML: magic extensible markup language.
Fig. 5. Example user interfaces for smart home system.
The Design and Implementation of an Energy-Smart Home in Korea
Jin Xiao and Raouf Boutaba 209 http://jcse.kiise.org
III. SMART ENERGY MANAGEMENT AT HOME
Fig. 5 shows the home screen of our home system,
which contains the home layout and appliance icons. This
is one of the essential UIs for information presentation
and manipulation for home energy management. The user
can freely select different rooms on the layout, and the
controllable appliances in each room are automatically
loaded and displayed. The user can then interact with
each of the specific objects by interacting with their cor-
responding icons. In the backend, there are specific appli-
ance object models that abstract the metering, control,
and management capabilities of each appliance in the
knowledge base. It is through these appliance object
models that the energy manager (an instance of the ser-
vice manager) can operate. The adaptors of the system
actually interact with the hardware device, which consists
of a suite of energy meters with built-in electric relays,
communicating via ZigBee network through a ZigBee
coordinator and ZigBee sensor modules (Fig. 6).
The energy management is also responsible for provid-
ing historical information on the various aspects of
energy consumption and cost. Two primary information
presentations are currently supported: a graph view and
an appliance schedule (Fig. 7). The graph view supports
dynamic ranging of the data reporting granularity (e.g.,
hour, day, week, etc.), as well as smooth sliding of the
reporting period (e.g., current week, past week, etc.).
Dynamic animations of the value bars provide convenient
visual cues to the user about the changes in energy con-
sumption, selected appliance usage history, and energy
cost. The appliance schedule is a calendar-style appliance
usage chart that depicts the appliance usage data, and
allows for user-directed appliance scheduling. Through
simple, one-touch editing on smartphones, the users can
modify appliance schedules, and the system will auto-
matically turn appliances on or off according to these
specific schedules. In addition, aggregate appliance con-
trol profiles are created for homes for even more dynamic
and easier control over groups of appliances. Some exam-
ple profiles are sleep mode, energy saving mode, study
mode, and away mode.
One aspect of energy management where intelligent
automation can help greatly is the scheduling of appli-
ances. Although with the capability to create appliance
schedule profiles, the home’s task of appliance manage-
ment is simplified, this capacity still does not provide
users with scheduling intelligence where fine-grained con-
trol and adjustment are necessary (e.g., re-adjusting appli-
ance usage so as not to exceed hourly or daily energy
quotas, to minimize peak-time energy usage due to higher
prices, etc.). Accordingly, our energy management imple-
ments a suite of smart appliance and environmental con-
trol algorithms called CODREX [6]. CODREX provides
a powerful set of algorithms that allows for near-optimal
appliance scheduling under a diverse set of objectives: userFig. 6. Energy meter and relay deployment (ZigBee network).
Fig. 7. (a) Energy chart view and (b) energy schedule view.
Journal of Computing Science and Engineering, Vol. 7, No. 3, September 2013, pp. 204-210
http://dx.doi.org/10.5626/JCSE.2013.7.3.204 210 Jin Xiao and Raouf Boutaba
comfort constraints, energy pricing, optimization objec-
tives (energy and price). It also automatically adjusts home
heating, ventilation, and cooling operations based on the
same constraint sets.
IV. CONCLUSION
We have presented the design, architecture, and imple-
mentation of a smart home system for future home auto-
mation and comfortable living assistance. In particular,
we have focused on a set of design criteria, and discussed
how our system design reflects them. As the system
design space is rather large, we focused on showcasing
some of the energy management aspects. The first round
of our smart home system development is near comple-
tion, and will be tested in real home usage scenarios in
our POSTECH test home in the coming months.
REFERENCES
1. J. Y. Son, J. H. Park, K. D. Moon, and Y. H. Lee, “Resource-
aware smart home management system by constructing
resource relation graph,” IEEE Transactions on Consumer
Electronics, vol. 57, no. 3, pp. 1112-1119, 2011.
2. D. M. Han and J. H. Lim, “Smart home energy manage-
ment system using IEEE 802.15.4 and ZigBee,” IEEE
Transactions on Consumer Electronics, vol. 56, no. 3, pp.
1403-1410, 2010.
3. Spring framework, http://www.springsource.org/.
4. Drools: the business logic integration platform, http://www.
jboss.org/drools/.
5. BlazeDS: the server-based Java remoting and Web messaging
framework, http://sourceforge.net/adobe/blazeds/wiki/Home/.
6. J. Xiao, J. Li, R. Boutaba, and J. W. Hong, “Comfort-aware
home energy management under market-based demand-
response,” in Proceedings of the 8th International Confer-
ence on Network and Service Management, Las Vegas, NV,
2012, pp. 10-18.
Jin Xiao
Jin Xiao received the Ph.D. degree in computer science from the University of Waterloo, Canada, in 2010. Heis currently a Research Professor at Pohang University of Science and Technology (POSTECH), Korea. Hisresearch interests include network and service management, service automation, network economics, smartgrid, smart home and energy management of smartphones. He has received several best paper and posterawards such as the Fred W. Ellersick Prize in 2008.
Raouf Boutaba
Raouf Boutaba received the M.Sc. and Ph.D. degrees in computer science from the University Pierre & MarieCurie, Paris, in 1990 and 1994, respectively. He is currently a Professor of computer science at University ofWaterloo, Canada and a Distinguished Visiting Professor at the Pohang University of Science and Technology(POSTECH), Korea. His research interests include control and management of networks and distributedsystems. He has received several best paper awards and other recognitions such as the Premier’s ResearchExcellence Award, the IEEE Hal Sobol Award in 2007, the Fred W. Ellersick Prize in 2008, the Joe LoCiceroAward and the Dan Stokesbury Award in 2009. He is a fellow of the IEEE.